Lab 06 - Video Latent / Frame Dimension

Overview

Lab 06: Video Latent / Frame Dimension

Annotated code reading lab. Running code is optional.

Related handbook section

Video Latent / Frame Dimension

This lab maps directly to the handbook section. Read the related handbook section first, then use the lab page and starter file to connect the concept to concrete variables, shapes, APIs, and interview-ready explanations.

Open handbook section Open starter file

Concept Goal

Video Latent / Frame Dimension

Make frame/time dimension explicit before reading video generation code.

Mental Model

Mechanism to keep in mind

`F` controls temporal length in this reading example.
`latent_shape` grows with frames, but rank/order/channel count are model- and pipeline-specific.
`temporal_attention` or 3D blocks may use frame relationships depending on architecture.

Annotated Code Preview

Starter preview

Excerpt from code/lab-06-video-latent-frame-dimension/video_latents.py. The linked starter file is the source of truth.

Open starter file

# Video Latent / Frame Dimension
# Annotated reading material. Running this file is optional.
# Source-of-truth focus: Make frame/time dimension explicit before reading video generation code.

B, C, F, H, W = 1, 16, 24, 90, 160  # example layout, not a universal contract
latent = (B, C, F, H, W)
per_frame_view = (B * F, C, H, W)  # implementation view, same video semantics
sequence_tokens = F * H * W

# What to explain while reading:
# - F controls temporal length.
# - latent_shape grows with frames, but exact rank/order/channel count are model-specific.
# - temporal_attention or 3D blocks may use frame relationships depending on architecture.
#
# Common traps:
# - Video generation is not independent image generation per frame.
# - Do not confuse frames with batch.

Line-by-line Explanation

What each block is doing

Setup / contract: `F` controls temporal length.
Main transition: `latent_shape` grows with frames.
Interview hook: `temporal_attention` or 3D blocks use frame relationships.

What to Notice

Reading checkpoints

Video memory grows quickly with frames.
Temporal consistency is a modeling problem, not just postprocess.
Frame axis may be flattened for implementation.

Common Misunderstandings

What this lab prevents

Video generation is not independent image generation per frame.
Do not confuse frames with batch.

Interview Explanation

How to say it out loud

Make frame/time dimension explicit before reading video generation code. Then explain the code by naming the state being transformed, the axis or shape that matters, and the tradeoff that would appear in a real system.

External intuition notes

Additional intuition

Use official docs and papers for API behavior and factual claims; use blogs only to improve the mental picture.
If support matrices, performance behavior or backend choices are version-sensitive, check current docs before repeating them.
A strong interview answer names the state object, the shape or axis it changes, and the tradeoff it creates.

InfraLens